RoboEdit: Turning Human Manipulation Videos into Scalable Robot Experience
- Published
- Source
- arXiv
- Paper number
- 958
- Field
- Robotics
- arXiv ID
- 2608.18948
Key points
- The key design choice is to edit human videos directly rather than generate them anew, preserving the original scene context and physical consistency.
- Cross-embodiment adaptation modules allow a single editor to cover the appearance, kinematics, and contact dynamics of seven robot embodiments.
- A 3D Robot-State Decoder recovers the robot hand state in every frame, providing structured supervision for downstream learning and control.
- The automatic RoboEdit-ADC pipeline uses depth normalization and physics-based refinement to reduce artifacts such as object interpenetration, floating contacts, and temporal jitter.
- The authors built RoboEdit-14M, a dataset of 174,000 paired videos comprising 14 million frames across seven robot embodiments, and showed that it supports training real-world manipulation policies.
Paper links
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